The aim of the study was to determine the most common precipitating factors and symptoms of diabetic ketoacidosis and the possible difference according to age, gender and severity of diabetic ketoacidosis. Medical records from January 1, 2017 until...

Aim: To examine dietary iodine intake and urinary iodine concentration in young adults, as well as the relationship between iodine concentration in urine and muscle mass and speed of muscle reaction. Methods: Seventy euthyroid participants between the ages of 18 and 24 participated, 35 (50%) male and 35 (50%) female. The participants were young adults who live, study and work in the region of Osijek, Osijek-Baranja County, Croatia. Exclusion criteria were diagnosed thyroid disease and iodine metabolism disorder. Spot urine samples were analysed, dietary iodine intake was assessed, muscle mass and reaction speed were measured. Results: The median concentration of iodine in urine was 120.77 μg/L, and the estimated iodine intake was 624.66 μg. The median muscle mass was 32.55%, and the median speed of muscle reaction 274.5 ms. A significant, negative and weak correlation between iodine concentration in urine and speed of muscle reaction was observed (Rho = -0.289). Other associations of urine iodine concentration and muscle mass percentage, muscle tissue and reaction speed were not significant Conclusion: The estimated dietary iodine intake was higher than the reference values of the World Health Organization. The urinary iodine concentration was within the reference values. Urinary iodine concentration was not related to muscle mass. Urinary iodine concentration in urine was significantly, negatively and weakly related to reaction speed.

Pheochromocytomas are rare neuroendocrine tumors that originate in chromaffin cells of the adrenal medulla and excessively secrete catecholamines, which leads to a multitude of different symptoms. The most common symptoms include headaches, palpita...

The main aim of this study was to examine the association of glucose and sodium level with diagnosis and disease outcome of critically ill patients. Glucose and sodium concentrations of 283 patients admitted in critical condition to the Intensive C...

Aim. Comorbidities pose a major challenge for 21st century medicine. The mutual pathophysiological effect of one disease on another can significantly affect their course and prognosis. The aims of this study were to examine the frequency of comorbidities and the most common psychiatric and somatic comorbidities and to determine the difference in the incidence of certain diseases by gender and age.Methods. Data were recorded in several groups: demographic characteristics, psychiatric and somatic diagnoses classified according to gender, age, and the legally determined ability to work, and correlations of somatic and psychiatric diagnoses.Results. The most common psychiatric diagnoses in men were post-traumatic stress disorder (PTSD) (25%) and alcoholism (23%), while in women these were recurrent depressive disorder (19%) and psychosis (10%). A statistically significant difference was found in the incidence of alcoholism and PTSD, which are more common in men than in women. The most common somatic diseases in both sexes were arterial hypertension (M = 33%, F = 46%) and diabetes mellitus (M = 18%, F = 32%). Statistically significant differences were found in the frequency of hypertension (p = 0.03) and epilepsy (p = 0.002), which are more common in men. The ratio alcoholism-hypertension (p = 0.03), alcoholism-diabetes (p < 0.0001), alcoholism-COPD (p < 0.001) was statistically significant.Conclusion. It is extremely important to improve the multidisciplinary approach and cooperation in treatment in order to reduce the number of hospitalizations, emergency interventions and suicides and to improve the patients’ quality of life and life expectancy.

This paper examines how data preparation affects machine-learning classifiers for diabetes-risk prediction using the Pima Indians Diabetes Database. Three preprocessing methods are considered: imputing invalid zeros, handling outliers, and data scaling. Nine algorithms are evaluated on this dataset: linear/probabilistic baselines (Logistic Regression, Gaussian Naive Bayes), distance-based methods (KNN, Support Vector Machines), a single tree-based model (Decision Tree), and tree ensembles (Random Forest, Gradient Boosting, XGBClassifier, LightGBM). Median imputation of invalid zeros yields the largest and most consistent gains in accuracy and AUC. Outlier handling uses interquartile-range filtering, with Local Outlier Factor as an auxiliary indicator; effects are modest for accuracy and small, model-dependent for AUC. Scaling offers targeted benefits: for KNN, robust scaling can slightly alter performance and may reduce AUC relative to median-only imputation in this setup; SVM shows modest gains, while tree ensembles are comparatively insensitive overall. Ensembles achieve the highest performance and remain robust under minimal preparation, while simpler models benefit most from pipelines combining median imputation, careful outlier handling, and appropriate scaling. Hyperparameter tuning yields small to substantial gains—large for Decision Trees—while leaving ensemble rankings largely unchanged. Overall, results highlight the centrality of median imputation and the selective value of scaling for distance-based classifiers in diabetes-risk prediction.